{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a89adf60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_csv, Grouper, DataFrame, concat\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.ar_model import AutoReg\n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import seaborn as sns\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from statsmodels.tsa.api import SimpleExpSmoothing\n",
    "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b11b9864",
   "metadata": {},
   "outputs": [],
   "source": [
    "us_gdp_data = pd.read_csv('./data/GDPUS.csv', header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f6a3ba90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['1929-12-31', '1930-12-31', '1931-12-31', '1932-12-31',\n",
      "               '1933-12-31', '1934-12-31', '1935-12-31', '1936-12-31',\n",
      "               '1937-12-31', '1938-12-31', '1939-12-31', '1940-12-31',\n",
      "               '1941-12-31', '1942-12-31', '1943-12-31', '1944-12-31',\n",
      "               '1945-12-31', '1946-12-31', '1947-12-31', '1948-12-31',\n",
      "               '1949-12-31', '1950-12-31', '1951-12-31', '1952-12-31',\n",
      "               '1953-12-31', '1954-12-31', '1955-12-31', '1956-12-31',\n",
      "               '1957-12-31', '1958-12-31', '1959-12-31', '1960-12-31',\n",
      "               '1961-12-31', '1962-12-31', '1963-12-31', '1964-12-31',\n",
      "               '1965-12-31', '1966-12-31', '1967-12-31', '1968-12-31',\n",
      "               '1969-12-31', '1970-12-31', '1971-12-31', '1972-12-31',\n",
      "               '1973-12-31', '1974-12-31', '1975-12-31', '1976-12-31',\n",
      "               '1977-12-31', '1978-12-31', '1979-12-31', '1980-12-31',\n",
      "               '1981-12-31', '1982-12-31', '1983-12-31', '1984-12-31',\n",
      "               '1985-12-31', '1986-12-31', '1987-12-31', '1988-12-31',\n",
      "               '1989-12-31', '1990-12-31', '1991-12-31'],\n",
      "              dtype='datetime64[ns]', freq='A-DEC')\n"
     ]
    }
   ],
   "source": [
    "date_rng = pd.date_range(start='1/1/1929', end='31/12/1991',freq='A')\n",
    "print(date_rng)\n",
    "us_gdp_data['TimeIndex'] = pd.DataFrame(date_rng,columns=['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "37448ee5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(us_gdp_data.TimeIndex, us_gdp_data.GDP)\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4cbc0ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "mvg_avg_us_gdp = us_gdp_data.copy()\n",
    "#calculating the rolling mean - with window 5\n",
    "mvg_avg_us_gdp['moving_avg_forecast'] = us_gdp_data['GDP'].rolling(5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "72e8ac3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(us_gdp_data['GDP'], label='US GDP')\n",
    "plt.plot(mvg_avg_us_gdp['moving_avg_forecast'], label='USGDP MA(5)')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "946c9b4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "url='opsd_germany_daily.csv'\n",
    "data = pd.read_csv(url,sep=\",\")\n",
    "data['Consumption'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8ae0b948",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-value:  4.7440549018424435e-08\n"
     ]
    }
   ],
   "source": [
    "data_stationarity_test = adfuller(data['Consumption'],autolag='AIC')\n",
    "print(\"P-value: \", data_stationarity_test[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b145fed8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pacf = plot_pacf(data['Consumption'], lags=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "15b03426",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = data['Consumption'][:len(data)-100]\n",
    "test_df = data['Consumption'][len(data)-100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9599c27a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vadi_\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\ar_model.py:248: FutureWarning: The parameter names will change after 0.12 is released. Set old_names to False to use the new names now. Set old_names to True to use the old names. \n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model_ar = AutoReg(train_df, lags=8).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ab8cbba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            AutoReg Model Results                             \n",
      "==============================================================================\n",
      "Dep. Variable:            Consumption   No. Observations:                 4283\n",
      "Model:                     AutoReg(8)   Log Likelihood              -24231.812\n",
      "Method:               Conditional MLE   S.D. of innovations             70.058\n",
      "Date:                Tue, 21 Mar 2023   AIC                              8.503\n",
      "Time:                        19:53:05   BIC                              8.518\n",
      "Sample:                             8   HQIC                             8.509\n",
      "                                 4283                                         \n",
      "==================================================================================\n",
      "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "intercept        121.2792     14.444      8.397      0.000      92.969     149.589\n",
      "Consumption.L1     0.6393      0.013     47.751      0.000       0.613       0.666\n",
      "Consumption.L2    -0.0966      0.011     -8.424      0.000      -0.119      -0.074\n",
      "Consumption.L3     0.0677      0.012      5.879      0.000       0.045       0.090\n",
      "Consumption.L4    -0.0538      0.012     -4.653      0.000      -0.076      -0.031\n",
      "Consumption.L5    -0.0092      0.012     -0.793      0.428      -0.032       0.014\n",
      "Consumption.L6     0.0619      0.012      5.371      0.000       0.039       0.085\n",
      "Consumption.L7     0.7832      0.011     68.283      0.000       0.761       0.806\n",
      "Consumption.L8    -0.4833      0.013    -36.107      0.000      -0.510      -0.457\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1           -0.9434           -0.4695j            1.0538           -0.4265\n",
      "AR.2           -0.9434           +0.4695j            1.0538            0.4265\n",
      "AR.3           -0.2332           -0.9929j            1.0199           -0.2867\n",
      "AR.4           -0.2332           +0.9929j            1.0199            0.2867\n",
      "AR.5            0.6323           -0.7958j            1.0164           -0.1431\n",
      "AR.6            0.6323           +0.7958j            1.0164            0.1431\n",
      "AR.7            1.0362           -0.0000j            1.0362           -0.0000\n",
      "AR.8            1.6730           -0.0000j            1.6730           -0.0000\n",
      "-----------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(model_ar.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d5527e25",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model_ar.predict(start=len(train_df),\n",
    "end=(len(data)-1), dynamic=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0ef57a10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f81fa7ee20>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "pyplot.plot(predictions)\n",
    "pyplot.plot(test_df, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5925ffb7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fa82d00e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Date       BTC-USD\n",
      "0  2017-12-31  14156.400391\n",
      "1  2018-01-01  13657.200195\n",
      "2  2018-01-02  14982.099609\n",
      "3  2018-01-03  15201.000000\n",
      "4  2018-01-04  15599.200195\n"
     ]
    }
   ],
   "source": [
    "btc_data = pd.read_csv(\"btc.csv\")\n",
    "print(btc_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b290ca64",
   "metadata": {},
   "outputs": [],
   "source": [
    "btc_data.index = pd.to_datetime(btc_data['Date'],\n",
    "format='%Y-%m-%d')\n",
    "del btc_data['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8fe9a1d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f822826490>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylabel('Price-BTC')\n",
    "plt.xlabel('Date')\n",
    "plt.xticks(rotation=45)\n",
    "plt.plot(btc_data.index, btc_data['BTC-USD'], )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f08189b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1036, 1)\n",
      "(31, 1)\n"
     ]
    }
   ],
   "source": [
    "train_data = btc_data[btc_data.index < pd.to_datetime(\"2020-11-01\", format='%Y-%m-%d')]\n",
    "test_data = btc_data[btc_data.index > pd.to_datetime(\"2020-11-01\", format='%Y-%m-%d')]\n",
    "print(train_data.shape)\n",
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2393d5e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_data, color = \"black\", label = 'Train')\n",
    "plt.plot(test_data, color = \"green\", label = 'Test')\n",
    "plt.ylabel('Price-BTC')\n",
    "plt.xlabel('Date')\n",
    "plt.xticks(rotation=35)\n",
    "plt.title(\"Train/Test split\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1ff9e325",
   "metadata": {},
   "outputs": [],
   "source": [
    "actuals = train_data['BTC-USD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "29b0c844",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vadi_\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\vadi_\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\vadi_\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    }
   ],
   "source": [
    "ARMA_model = ARIMA(actuals, order = (1, 0, 1))\n",
    "ARMA_model = ARMA_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9aee772f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vadi_\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:132: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.\n",
      "  date_key = Timestamp(key, freq=base_index.freq)\n"
     ]
    }
   ],
   "source": [
    "predictions = ARMA_model.get_forecast(len(test_data.index))\n",
    "predictions_df = predictions.conf_int(alpha = 0.05)\n",
    "predictions_df[\"Predictions\"] = ARMA_model.predict(start = predictions_df.index[0], end = predictions_df.index[-1])\n",
    "predictions_df.index = test_data.index\n",
    "predictions_arma = predictions_df[\"Predictions\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cd1acc7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_data, color = \"black\", label = 'Train')\n",
    "plt.plot(test_data, color = \"green\", label = 'Test')\n",
    "plt.ylabel('Price-BTC')\n",
    "plt.xlabel('Date')\n",
    "plt.xticks(rotation=35)\n",
    "plt.title(\"ARMA model predictions\")\n",
    "plt.plot(predictions_arma, color=\"red\", label = 'Predictions')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "870e58f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  4017.145069637629\n"
     ]
    }
   ],
   "source": [
    "rmse_arma = np.sqrt(mean_squared_error(test_data[\"BTC-USD\"].values, predictions_df[\"Predictions\"]))\n",
    "print(\"RMSE: \",rmse_arma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f01c7dbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# differencing\n",
    "ts_diff = actuals - actuals.shift(periods=4)\n",
    "ts_diff.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6193d1bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -6.124168\n",
      "p-value: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# checking for stationarity\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "result = adfuller(ts_diff)\n",
    "pval = result[1]\n",
    "print('ADF Statistic: %f' % result[0])\n",
    "print('p-value: %f' % result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f8d60f1e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vadi_\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:667: FutureWarning: fft=True will become the default after the release of the 0.12 release of statsmodels. To suppress this warning, explicitly set fft=False.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "lag_acf = acf(ts_diff, nlags=20)\n",
    "lag_pacf = pacf(ts_diff, nlags=20, method='ols')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "610822d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Autocorrelation Function')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ploting ACF:\n",
    "plt.figure(figsize = (15,5))\n",
    "plt.subplot(121)\n",
    "plt.stem(lag_acf)\n",
    "plt.axhline(y = 0, linestyle='--',color='black')\n",
    "plt.axhline(y = -1.96/np.sqrt(len(ts_diff)),linestyle='--',color='gray')\n",
    "plt.axhline(y = 1.96/np.sqrt(len(ts_diff)),linestyle='--',color='gray')\n",
    "plt.xticks(range(0,22,1))\n",
    "plt.xlabel('Lag')\n",
    "plt.ylabel('ACF')\n",
    "plt.title('Autocorrelation Function')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4e00c60b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting PACF:\n",
    "plt.figure(figsize = (15,5))\n",
    "plt.subplot(122)\n",
    "plt.stem(lag_pacf)\n",
    "plt.axhline(y = 0, linestyle = '--', color = 'black')\n",
    "plt.axhline(y =-1.96/np.sqrt(len(actuals)), linestyle = '--',color = 'gray')\n",
    "plt.axhline(y = 1.96/np.sqrt(len(actuals)),linestyle = '--',color = 'gray')\n",
    "plt.xlabel('Lag')\n",
    "plt.xticks(range(0,22,1))\n",
    "plt.ylabel('PACF')\n",
    "plt.title('Partial Autocorrelation Function')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "571ffdd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vadi_\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\vadi_\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\vadi_\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\vadi_\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "C:\\Users\\vadi_\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n"
     ]
    }
   ],
   "source": [
    "ARIMA_model = ARIMA(actuals, order = (10, 4, 1))\n",
    "ARIMA_model = ARIMA_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a91f695a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vadi_\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:132: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.\n",
      "  date_key = Timestamp(key, freq=base_index.freq)\n"
     ]
    }
   ],
   "source": [
    "predictions = ARIMA_model.get_forecast(len(test_data.index))\n",
    "predictions_df = predictions.conf_int(alpha = 0.05)\n",
    "predictions_df[\"Predictions\"] = ARIMA_model.predict(start =\n",
    "predictions_df.index[0], end = predictions_df.index[-1])\n",
    "predictions_df.index = test_data.index\n",
    "predictions_arima = predictions_df[\"Predictions\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a0574049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_data, color = \"black\", label = 'Train')\n",
    "plt.plot(test_data, color = \"green\", label = 'Test')\n",
    "plt.ylabel('Price-BTC')\n",
    "plt.xlabel('Date')\n",
    "plt.xticks(rotation=35)\n",
    "plt.title(\"ARIMA model predictions\")\n",
    "plt.plot(predictions_arima, color=\"red\", label = 'Predictions')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "089b4693",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  2895.312718157126\n"
     ]
    }
   ],
   "source": [
    "rmse_arima = np.sqrt(mean_squared_error(test_data[\"BTC-USD\"].\n",
    "values, predictions_df[\"Predictions\"]))\n",
    "print(\"RMSE: \",rmse_arima)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "96a5cccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def arima_model_evaluate(train_actuals, test_data, order):\n",
    "    # Model initalize and fit\n",
    "    ARIMA_model = ARIMA(actuals, order = order)\n",
    "    ARIMA_model = ARIMA_model.fit()\n",
    "    # Getting the predictions\n",
    "    predictions = ARIMA_model.get_forecast(len(test_data.index))\n",
    "    predictions_df = predictions.conf_int(alpha = 0.05)\n",
    "    predictions_df[\"Predictions\"] = ARIMA_model.predict(start = predictions_df.index[0], end = predictions_df.index[-1])\n",
    "    predictions_df.index = test_data.index\n",
    "    predictions_arima = predictions_df[\"Predictions\"]\n",
    "    # calculate RMSE score\n",
    "    rmse_score = np.sqrt(mean_squared_error(test_data[\"BTC-USD\"].values, predictions_df[\"Predictions\"]))\n",
    "    return rmse_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f3afb2e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_models(train_actuals, test_data, list_p_values,\n",
    "    list_d_values, list_q_values):\n",
    "    best_rmse, best_config = float(\"inf\"), None\n",
    "    for p in list_p_values:\n",
    "        for d in list_d_values:\n",
    "            for q in list_q_values:\n",
    "                arima_order = (p,d,q)\n",
    "                rmse = arima_model_evaluate(train_actuals,test_data, arima_order)\n",
    "                if rmse < best_rmse:\n",
    "                    best_rmse, best_config = rmse, arima_order\n",
    "                print('ARIMA%s RMSE=%.3f' % (arima_order,rmse))\n",
    "    print('Best Configuration: ARIMA%s , RMSE=%.3f' % (best_config, best_rmse))\n",
    "    return best_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d90f5bf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 0) RMSE=8973.268\n",
      "ARIMA(0, 0, 1) RMSE=8927.094\n",
      "ARIMA(0, 0, 2) RMSE=8895.924\n",
      "ARIMA(0, 0, 3) RMSE=8861.499\n",
      "ARIMA(0, 1, 0) RMSE=3527.133\n",
      "ARIMA(0, 1, 1) RMSE=3537.297\n",
      "ARIMA(0, 1, 2) RMSE=3519.475\n",
      "ARIMA(0, 1, 3) RMSE=3514.476\n",
      "ARIMA(0, 2, 0) RMSE=1112.565\n",
      "ARIMA(0, 2, 1) RMSE=3455.709\n",
      "ARIMA(0, 2, 2) RMSE=3315.249\n",
      "ARIMA(0, 2, 3) RMSE=3337.231\n",
      "ARIMA(0, 3, 0) RMSE=30160.941\n",
      "ARIMA(0, 3, 1) RMSE=887.423\n",
      "ARIMA(0, 3, 2) RMSE=3209.141\n",
      "ARIMA(0, 3, 3) RMSE=2970.229\n",
      "ARIMA(1, 0, 0) RMSE=4079.516\n",
      "ARIMA(1, 0, 1) RMSE=4017.145\n",
      "ARIMA(1, 0, 2) RMSE=4065.809\n",
      "ARIMA(1, 0, 3) RMSE=4087.934\n",
      "ARIMA(1, 1, 0) RMSE=3537.539\n",
      "ARIMA(1, 1, 1) RMSE=3535.791\n",
      "ARIMA(1, 1, 2) RMSE=3537.341\n",
      "ARIMA(1, 1, 3) RMSE=3504.703\n",
      "ARIMA(1, 2, 0) RMSE=725.218\n",
      "ARIMA(1, 2, 1) RMSE=3318.935\n",
      "ARIMA(1, 2, 2) RMSE=3507.106\n",
      "ARIMA(1, 2, 3) RMSE=3314.726\n",
      "ARIMA(1, 3, 0) RMSE=12360.360\n",
      "ARIMA(1, 3, 1) RMSE=727.351\n",
      "ARIMA(1, 3, 2) RMSE=2968.820\n",
      "ARIMA(1, 3, 3) RMSE=3019.434\n",
      "ARIMA(2, 0, 0) RMSE=4014.318\n",
      "ARIMA(2, 0, 1) RMSE=4022.540\n",
      "ARIMA(2, 0, 2) RMSE=4062.346\n",
      "ARIMA(2, 0, 3) RMSE=4088.628\n",
      "ARIMA(2, 1, 0) RMSE=3522.798\n",
      "ARIMA(2, 1, 1) RMSE=3509.829\n",
      "ARIMA(2, 1, 2) RMSE=3523.407\n",
      "ARIMA(2, 1, 3) RMSE=3517.972\n",
      "ARIMA(2, 2, 0) RMSE=748.267\n",
      "ARIMA(2, 2, 1) RMSE=3498.685\n",
      "ARIMA(2, 2, 2) RMSE=3514.870\n",
      "ARIMA(2, 2, 3) RMSE=3310.798\n",
      "ARIMA(2, 3, 0) RMSE=33486.993\n",
      "ARIMA(2, 3, 1) RMSE=797.942\n",
      "ARIMA(2, 3, 2) RMSE=2979.751\n",
      "ARIMA(2, 3, 3) RMSE=2965.450\n",
      "ARIMA(3, 0, 0) RMSE=4060.745\n",
      "ARIMA(3, 0, 1) RMSE=4114.216\n",
      "ARIMA(3, 0, 2) RMSE=4060.737\n",
      "ARIMA(3, 0, 3) RMSE=3810.374\n",
      "ARIMA(3, 1, 0) RMSE=3509.046\n",
      "ARIMA(3, 1, 1) RMSE=3499.516\n",
      "ARIMA(3, 1, 2) RMSE=3520.499\n",
      "ARIMA(3, 1, 3) RMSE=3521.356\n",
      "ARIMA(3, 2, 0) RMSE=1333.102\n",
      "ARIMA(3, 2, 1) RMSE=3482.502\n",
      "ARIMA(3, 2, 2) RMSE=3451.985\n",
      "ARIMA(3, 2, 3) RMSE=3285.008\n",
      "ARIMA(3, 3, 0) RMSE=14358.749\n",
      "ARIMA(3, 3, 1) RMSE=1477.509\n",
      "ARIMA(3, 3, 2) RMSE=3142.936\n",
      "ARIMA(3, 3, 3) RMSE=2957.573\n",
      "Best Configuration: ARIMA(1, 2, 0) , RMSE=725.218\n"
     ]
    }
   ],
   "source": [
    "p_values = range(0, 4)\n",
    "d_values = range(0, 4)\n",
    "q_values = range(0, 4)\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "best_config = evaluate_models(actuals,test_data, p_values,d_values, q_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2d28a270",
   "metadata": {},
   "outputs": [],
   "source": [
    "ARIMA_model = ARIMA(actuals, order = best_config)\n",
    "ARIMA_model = ARIMA_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "36542c40",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = ARIMA_model.get_forecast(len(test_data.index))\n",
    "predictions_df = predictions.conf_int(alpha = 0.05)\n",
    "predictions_df[\"Predictions\"] = ARIMA_model.predict(start =\n",
    "predictions_df.index[0], end = predictions_df.index[-1])\n",
    "predictions_df.index = test_data.index\n",
    "predictions_arima = predictions_df[\"Predictions\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8a898f00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_data, color = \"black\", label = 'Train')\n",
    "plt.plot(test_data, color = \"green\", label = 'Test')\n",
    "plt.ylabel('Price-BTC')\n",
    "plt.xlabel('Date')\n",
    "plt.xticks(rotation=35)\n",
    "plt.title(\"ARIMA model predictions\")\n",
    "plt.plot(predictions_arima, color=\"red\", label = 'Predictions')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "502c49f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  725.2180143501593\n"
     ]
    }
   ],
   "source": [
    "rmse_arima = np.sqrt(mean_squared_error(test_data[\"BTC-USD\"].\n",
    "values, predictions_df[\"Predictions\"]))\n",
    "print(\"RMSE: \",rmse_arima)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1b82861",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3bb9a5d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "SARIMA_model = SARIMAX(actuals, order = (1, 2, 0), seasonal_order=(2,2,2,12))\n",
    "SARIMA_model = SARIMA_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d94b69a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = SARIMA_model.get_forecast(len(test_data.index))\n",
    "predictions_df = predictions.conf_int(alpha = 0.05)\n",
    "predictions_df[\"Predictions\"] = SARIMA_model.predict(start =predictions_df.index[0], end = predictions_df.index[-1])\n",
    "predictions_df.index = test_data.index\n",
    "predictions_sarima = predictions_df[\"Predictions\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0330ffe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_data, color = \"black\", label = 'Train')\n",
    "plt.plot(test_data, color = \"green\", label = 'Test')\n",
    "plt.ylabel('Price-BTC')\n",
    "plt.xlabel('Date')\n",
    "plt.xticks(rotation=35)\n",
    "plt.title(\"SARIMA model predictions\")\n",
    "plt.plot(predictions_sarima, color=\"red\", label ='Predictions')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "48547a5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  1050.157033576061\n"
     ]
    }
   ],
   "source": [
    "rmse_sarima = np.sqrt(mean_squared_error(test_data[\"BTC-USD\"].values, predictions_df[\"Predictions\"]))\n",
    "print(\"RMSE: \",rmse_sarima)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd47ba6a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6bee7928",
   "metadata": {},
   "outputs": [],
   "source": [
    "SES_model = SimpleExpSmoothing(actuals)\n",
    "SES_model = SES_model.fit(smoothing_level=0.8,optimized=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "01fa42d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_ses = SES_model.forecast(len(test_data.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3ea6c002",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOL while scanning string literal (Temp/ipykernel_30928/3180726283.py, line 6)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\vadi_\\AppData\\Local\\Temp/ipykernel_30928/3180726283.py\"\u001b[1;36m, line \u001b[1;32m6\u001b[0m\n\u001b[1;33m    plt.title(\"SImple Exponential Smoothing (SES) model\u001b[0m\n\u001b[1;37m                                                       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m EOL while scanning string literal\n"
     ]
    }
   ],
   "source": [
    "plt.plot(train_data, color = \"black\", label = 'Train')\n",
    "plt.plot(test_data, color = \"green\", label = 'Test')\n",
    "plt.ylabel('Price-BTC')\n",
    "plt.xlabel('Date')\n",
    "plt.xticks(rotation=35)\n",
    "plt.title(\"SImple Exponential Smoothing (SES) model\n",
    "predictions\")\n",
    "plt.plot(predictions_ses, color='red', label = 'Predictions')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bc0a79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_ses = np.sqrt(mean_squared_error(test_data[\"BTC-USD\"].\n",
    "values, predictions_ses))\n",
    "print(\"RMSE: \",rmse_ses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81c671b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "HW_model = ExponentialSmoothing(actuals, trend='add')\n",
    "HW_model = HW_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b252f56a",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_hw = HW_model.forecast(len(test_data.index))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21a9b55c",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(train_data, color = \"black\", label = 'Train')\n",
    "plt.plot(test_data, color = \"green\", label = 'Test')\n",
    "plt.ylabel('Price-BTC')\n",
    "plt.xlabel('Date')\n",
    "plt.xticks(rotation=35)\n",
    "plt.title(\"HW model predictions\")\n",
    "plt.plot(predictions_hw, color='red', label = 'Predictions')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a6471bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_hw = np.sqrt(mean_squared_error(test_data[\"BTC-USD\"].\n",
    "values, predictions_hw))\n",
    "print(\"RMSE: \",rmse_hw)"
   ]
  }
 ],
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